Abstract
In this paper, we present a novel self-generation, supervised character recognition algorithm based on adaptive resonance theory (ART) artificial neural network (ANN) and delta-bar-delta method. By combining two methods, the proposed algorithm can reduce noise problem in the ART ANN and the local minima problem in the delta-bar-delta method. The proposed method can extend itself based on new information contained in input patterns that require nodes of hidden layers in neural networks and effectively find characters. We experiment with various real-world documents such as a student ID and an identifier on a container. The experimental results show that the proposed self-generation. ART algorithm reduces the possibility of local minima and accelerates learning speed compared with existing.
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© 2006 Springer-Verlag Berlin Heidelberg
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Kim, T., Lee, S., Paik, J. (2006). Self-generation ART Neural Network for Character Recognition. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_41
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DOI: https://doi.org/10.1007/11760023_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
Online ISBN: 978-3-540-34438-4
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